Teaching
My teaching combines rigorous methodological training with substantive questions across the social sciences, emphasizing transparent, reproducible, and policy-relevant research. My methods courses use real-world data and modern computational tools, and I can teach substantive courses on subjects including political economy, development, institutions, and formal theory. Mentoring is a core part of my teaching: I currently supervise more than a dozen undergraduate researchers at NYU, building on earlier experience mentoring research assistants at WashU.
Courses Taught
New York University
Instructor of Record
- Principles of Data Science (Undergraduate), Spring 2026
- Practical Training for Data Science (Graduate), Spring, Summer, Fall 2026
Washington University in St. Louis
Instructor
- Python Workshop (Graduate)
Teaching Assistant
- Causal Inference (Graduate)
- Data Science for Politics (Undergraduate)
- Terrorism and Counterterrorism (Undergraduate)
- International Politics (Undergraduate)
Teaching Interests
Data Science & Research Methods: Introduction to Data Science; Causal Inference; Bayesian Data Analysis; Time Series and Panel Data; Machine Learning; Research Design; Computational Social Science
Political Science and Public Policy: International Political Economy; Political Economy of Development; Formal Theory; Institutions and Governance; Global Politics; Politics of South Asia